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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Learning Translation Templates From Bilingual Translation Examples Ilyas Cicekli; H. Altay Güvenir In: http://www.cs.bilkent.edu.tr/~ilyas/PDF/appint2001.pdf (2000) BASE Show details 2 Learning Translation Templates from Examples H. Altay Güvenir; Ilyas Cicekli In: http://www.cs.bilkent.edu.tr/~guvenir/publications/IS_23_6_353.ps (1998) BASE Show details 3 Learning Translation Templates from Examples, in H. Altay Güvenir; Ilyas Cicekli In: http://www.cs.bilkent.edu.tr/~ilyas/PDF/wits96.pdf (1998) BASE Show details 4 Learning Translation Rules From A Bilingual Corpus Ilyas Cicekli; H. Altay Güvenir In: http://www.cs.bilkent.edu.tr/~ilyas/PDF/nemlap96.pdf (1996) BASE Show details 5 c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. Learning Translation Templates from Bilingual Translation Examples Ilyas Cicekli; H. Altay Güvenir In: http://www.cs.bilkent.edu.tr/~guvenir/publications/AI-15-1-57.pdf Abstract: Abstract. A mechanism for learning lexical correspondences between two languages from sets of translated sentence pairs is presented. These lexical level correspondences are learned using analogical reasoning between two translation examples. Given two translation examples, the similar parts of the sentences in the source language must correspond to the similar parts of the sentences in the target language. Similarly, the different parts must correspond to the respective parts in the translated sentences. The correspondences between similarities and between differences are learned in the form of translation templates. A translation template is a generalized translation exemplar pair where some components are generalized by replacing them with variables in both sentences and establishing bindings between these variables. The learned translation templates are obtained by replacing differences or similarities by variables. This approach has been implemented and tested on a set of sample training datasets and produced promising results for further investigation. Keyword: corpus-based machine translation; example-based machine translation; exemplar based machine learning URL: http://www.cs.bilkent.edu.tr/~guvenir/publications/AI-15-1-57.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.65.7395 BASE Hide details 6 arXiv:cmp-lg/9607027v1 26 Jul 1996Learning Translation Rules From A Bilingual Corpus ⋆ Ilyas Cicekli; H. Altay Güvenir In: http://arxiv.org/pdf/cmp-lg/9607027v1.pdf BASE Show details

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